Dynamic partitioning method and device for power distribution network, electronic equipment and storage medium

Abstract

The invention discloses a dynamic partitioning method and device for a power distribution network, electronic equipment and a storage medium, and the method comprises the steps: obtaining a scheduling plan of a typical day, inputting the scheduling plan into a power distribution network partitioning model, generating a first partitioning scheme of each scheduling moment, judging whether the voltage of each node at the first scheduling moment exceeds a preset range or not, and if yes, generating a second partitioning scheme; if the index exceeds the preset range, the index weight is adjusted to update the power distribution network partition model, a second partition scheme at the first scheduling moment is generated, and an actual partition scheme at the first scheduling moment is determined by comparing the score difference of the two partition schemes; and for other subsequent scheduling moments, determining an actual partition scheme of the current scheduling moment based on a difference value between the actual partition score and the actual partition score of the previous moment. By implementing the method, dynamic partitioning of the power distribution network can be realized, and the operation efficiency and reliability of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network partitioning, and in particular to a method, device, electronic equipment and storage medium for dynamic partitioning of a distribution network. Background Art

[0002] Reasonable zoning of distribution networks is crucial to improving the operating efficiency and stability of power systems. Scientific zoning can not only optimize the distribution of electric energy, but also effectively reduce network losses, improve power supply quality, and enhance the system's responsiveness to load fluctuations and renewable energy access. Through reasonable zoning, the distribution network can achieve efficient allocation of resources between regions, ensure the balance of power supply and demand, and thus improve the economy and reliability of the overall power grid.

[0003] However, existing distribution network partitioning technologies mostly rely on typical operation scenarios of the power grid or use typical time data for static partitioning. This static partitioning method is often difficult to effectively cope with the complexity and variability of the distribution network in actual operation. The operation status of the distribution network is affected by many factors, including load characteristics, climate change, and fluctuations in renewable energy generation. Under the combined effect of these factors, the traditional partitioning structure appears to be very rigid and lacks sufficient flexibility.

[0004] With the rapid development of renewable energy such as photovoltaic power generation, distribution networks are facing frequent fluctuations in load and power generation. This volatility causes the traditional partition structure to show obvious deficiencies in actual operation and is difficult to adjust in real time. This inflexible structure often appears to be powerless when dealing with emergencies and diversified operating scenarios, and is unable to adapt to rapidly changing electricity demand and supply conditions. In addition, static partitioning may cause some areas to be overloaded while other areas are idle, which not only affects the power supply reliability of the power grid, but also restricts the economic operation of the power system and increases the operating costs of the system. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic device and storage medium for dynamic partitioning of a distribution network. By implementing the present invention, a distribution network partitioning model can be constructed to generate an optimal partitioning scheme for dynamically adjusting the partitioning results of each area of ​​the distribution network, thereby improving the overall management efficiency and stability of the power grid.

[0006] An embodiment of the present invention provides a method for dynamic partitioning of a distribution network, comprising:

[0007] Obtain the dispatch plan for a typical day of the distribution network;

[0008] Inputting the dispatch plan into the distribution network partition model to generate the first partition scheme and the first partition score at each dispatching moment when the partition scheme score is the largest;

[0009] Obtain the first voltage of each node at the first scheduling time during the operation day of the distribution network;

[0010] Determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, adjust the weight of each evaluation index in the distribution network partition model to obtain an updated distribution network partition model, input the scheduling plan into the updated distribution network partition model, and generate a second partition scheme and a second partition score at the first scheduling time when the partition scheme score is the largest; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range;

[0011] Calculate the difference between the first partition score and the second partition score at the first scheduling time. If the difference exceeds the set threshold, the corresponding second partition scheme is used as the actual partition scheme at the first scheduling time. If the difference does not exceed the set threshold, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling time.

[0012] If there is no node exceeding the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment;

[0013] For other scheduling moments except the first scheduling moment, the difference between the first partition score and the actual partition score at the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

[0014] Furthermore, obtaining a typical day's dispatch plan for the distribution network includes:

[0015] Obtain load data, distributed photovoltaic data and distribution network operation data on a typical day of the distribution network;

[0016] Among them, load data includes: load active power of the node; distributed photovoltaic data includes: predicted power of distributed photovoltaic of distributed power supply node and capacity of distributed photovoltaic of distributed power supply node; distribution network operation data includes: topological information of distribution network, impedance of branch, current upper limit of branch, voltage upper limit of node, voltage lower limit of node, active power upper limit of node and active power lower limit of node;

[0017] Constructing an optimal power flow model of the distribution network according to the distribution network operation data of the typical day;

[0018] According to the load data of the typical day and the distributed photovoltaic prediction data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

[0019] Furthermore, the optimal power flow model of the distribution network includes an objective function and constraints;

[0020] The objective function includes:

[0021]

[0022] Where L is the network loss power of the distribution network; I ij，t is the current of branch ij at the dispatching time t; r ij is the resistance of branch ij; T is the scheduling time set; ε is the branch set;

[0023] The constraints include: node power balance constraints, Ohm's law constraints, branch head end power constraints, node voltage amplitude constraints, node injection power constraints, branch current amplitude constraints and distributed photovoltaic constraints.

[0024] Furthermore, the node power balance constraint is:

[0025]

[0026]

[0027] Among them, S jk，t is the active power of branch jk at the dispatching time t; S ij，t is the active power of branch ij at the dispatching time t; S j，t is the active power of node j at scheduling time t; z ij is the impedance of branch ij; I ij，t is the current of branch ij at the dispatching time t; is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the load active power of node j at scheduling time t;

[0028] The Ohm's law constraint is:

[0029] V i，t -V j，t =z ij I ij，t

[0030] Among them, V i，t is the voltage of node i at scheduling time t; V j，tis the voltage of node j at scheduling time t; z ij is the impedance of branch ij; I ij，t is the current of branch ij at the dispatching time t;

[0031] The power constraint at the head end of the branch is:

[0032]

[0033] Among them, S ij，t is the active power of branch ij at the dispatching time t; V i，t is the voltage of node i at scheduling time t; is the conjugate of the current of branch ij at the dispatching time t;

[0034] The node voltage amplitude constraint is:

[0035]

[0036] in, V i is the voltage lower limit of node i; is the voltage upper limit of node i; V i，t is the voltage of node i at scheduling time t;

[0037] The node injection power constraint is:

[0038]

[0039] in, S i is the upper limit of active power of node i; The lower limit of active power of node i; S i，t is the active power of node i at scheduling time t;

[0040] The branch current amplitude constraint is:

[0041]

[0042] Among them, I ij，t is the current of branch ij at the dispatching time t; is the upper limit of the current of branch ij;

[0043] The distributed photovoltaic constraints are:

[0044]

[0045] in, is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the predicted power of the distributed photovoltaic connected to node j at the scheduling time t.

[0046] Furthermore, the distribution network partition model includes an objective function;

[0047]

[0048] Among them, G is the partition scheme score; is the partition structure at the scheduling time t; I1 is the regional modularity index; ω1 is the weight of the regional modularity index; I2 is the load rate construction index; ω2 is the weight of the load rate construction index; I3 is the average load difference index; ω3 is the weight of the average load difference index; I4 is the average peak-to-valley difference rate index; ω4 is the weight of the average peak-to-valley difference rate index; I5 is the voltage regulation capability index; ω5 is the weight of the voltage regulation capability index; I6 is the power regulation capability index; ω6 is the weight of the power regulation capability index.

[0049] Furthermore, the regional modularity index is determined by the following formula:

[0050]

[0051] e ij =1-L ij / max((L)

[0052]

[0053]

[0054]

[0055] Among them, I1 is the regional modularity index; e ij is the electrical distance between node i and node j; m is the half value of the electrical distance between all nodes in the planning area; k i is the sum of the weights of node i; k j is the sum of the weights of node i and node j; δ(i, j) is a 0-1 variable indicating whether nodes i and j are located in the same region; L ij is the degree of association between node i and node j; max(L) is the maximum degree of association; d in is the influence degree of node n on node i; d jn is the influence degree of node n on node j; d ij is the influence degree of node j on node i; S VP，jj is the sensitivity in the sensitivity matrix associated with the voltage and active power of node j; S VQ，jj is the sensitivity in the sensitivity matrix associated with the voltage and reactive power of node j; S VP，ij is the sensitivity in the sensitivity matrix associated with the voltage and active power of node i and node j; S VQ，ijis the sensitivity in the sensitivity matrix associated with the voltage reactive power of node i and node j;

[0056] The load factor construction index is determined by the following formula:

[0057]

[0058]

[0059] Where I2 is the load factor structure index; N is the number of regions; s is the region index; f s is the load rate of each area; f av is the average load factor of each area;

[0060] The average load difference index is determined by the following formula:

[0061]

[0062] Where I3 is the average load difference index; N is the number of regions; s is the region index; P max is the maximum value of the maximum load in each area; P s max is the maximum load of the sth area;

[0063] The average peak-to-valley rate indicator is determined by the following formula:

[0064]

[0065] Among them, I4 is the average peak-to-valley difference rate index; N is the number of regions; s is the region index; f sp is the peak-to-valley difference rate of each region;

[0066] The voltage regulation capability index is determined by the following formula:

[0067]

[0068]

[0069] Among them, I5 is the voltage regulation capability index; N is the number of regions; s is the region index; is the voltage regulation capability of the region; ΔV i is the voltage regulation amount of node i in region s; is the maximum voltage regulation amount of node i in region s;

[0070] The power continuous regulation capability index is determined by the following formula:

[0071]

[0072]

[0073]

[0074] Among them, I6 is the power continuous regulation capability index; N is the number of regions; s is the region index; is the power regulation capability of region s; is the adjustable power of area s; is the net load of area s at dispatch time t; is the adjustable power of distributed photovoltaic in area s at scheduling time t; is the adjustable power of the flexible load in area s at the scheduling time t.

[0075] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0076] An embodiment of the present invention provides a dynamic partitioning device for a distribution network, comprising: a scheduling plan acquisition module, a first partitioning scheme generation module, a second partitioning scheme generation module and an actual partitioning scheme confirmation module.

[0077] The dispatch plan acquisition module is used to obtain the dispatch plan of a typical day of the distribution network;

[0078] The first partition scheme generating module is used to input the scheduling plan into the distribution network partition model to generate the first partition scheme and the first partition score at each scheduling moment when the partition scheme score is the largest;

[0079] The second partition scheme generating module is used to determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, the weight of each evaluation index in the distribution network partition model is adjusted to obtain an updated distribution network partition model, and the scheduling plan is input into the updated distribution network partition model to generate a second partition scheme and a second partition score at the first scheduling time when the partition scheme score is the largest; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range;

[0080] The actual partition scheme confirmation module is used to calculate the difference between the first partition score and the second partition score at the first scheduling moment. If the difference exceeds the set threshold, the corresponding second partition scheme is used as the actual partition scheme at the first scheduling moment; if the difference does not exceed the set threshold, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling moment; if there is no node exceeding the preset voltage range, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling moment; for other scheduling moments except the first scheduling moment, the difference between the first partition score and the actual partition score at the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

[0081] Furthermore, obtaining a typical day's dispatch plan for the distribution network includes:

[0082] Obtain load data, distributed photovoltaic data and distribution network operation data on a typical day of the distribution network;

[0083] Among them, load data includes: load active power of the node; distributed photovoltaic data includes: predicted power of distributed photovoltaic of distributed power supply node and capacity of distributed photovoltaic of distributed power supply node; distribution network operation data includes: topological information of distribution network, impedance of branch, current upper limit of branch, voltage upper limit of node, voltage lower limit of node, active power upper limit of node and active power lower limit of node;

[0084] Constructing an optimal power flow model of the distribution network according to the distribution network operation data of the typical day;

[0085] According to the load data of the typical day and the distributed photovoltaic prediction data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

[0086] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0087] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the dynamic partitioning method of the distribution network described in any one of the above method embodiments can be implemented.

[0088] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0089] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for dynamically partitioning a distribution network as described in any one of the above method embodiments can be implemented.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] Embodiments of the present invention provide a method, device, electronic device and storage medium for dynamic partitioning of a distribution network. The method constructs a highly self-balancing distribution network partition model through regional modularity index, regional load rate index, average load difference index, average peak-to-valley difference rate index, voltage regulation capability index, power regulation capability index and the indicator weights corresponding to all indicators, and solves the first partition scheme and the first partition score at each scheduling moment according to the scheduling plan of the typical day of the distribution network. The first partition scheme makes the internal adaptability of the partition stronger and effectively reduces the mutual interference between the regions; for the first scheduling moment of the operation day, it is judged whether the voltage exceeds the limit; in the case of voltage exceeding the limit, the indicator weight of the distribution network partition model is adjusted, and the second partition scheme and the second partition score are re-solved and generated, and it is determined whether the second partition scheme is to be used as the actual partition scheme of the distribution network at the first scheduling moment according to the difference between the first partition score and the second partition score, so as to ensure that the partition structure can respond to voltage fluctuations in time, quickly eliminate the risk of exceeding the limit, and thus realize the precise regulation of the distribution network; if the voltage exceeds the limit but the difference between the first partition score and the second partition score does not exceed the preset threshold, the first partition scheme is still selected instead of the second partition scheme after weight adjustment, indicating that the necessity of partition adjustment is not strong, and even if the voltage exceeds the limit, the original scheme has little effect on the overall stability and scheduling effect of the system. This selection is based on the setting of the difference threshold, which ensures that the system will not frequently adjust the partitions due to minor optimization effects, thereby reducing unnecessary partition switching. For other scheduling moments, the partition scheme is changed only when the difference between the first partition score at the current scheduling moment and the actual score at the previous scheduling moment exceeds the preset threshold. This can avoid frequent partition switching while ensuring the efficiency and consistency of the actual running partition scheme in different time periods, avoid the instability caused by frequent switching, reasonably control the frequency of partition switching, make operation and maintenance easier, and reduce the complexity of system scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a flow chart of a method for dynamic partitioning of a distribution network provided in one embodiment of the present invention.

[0093] Figure 2 It is a structural schematic diagram of a dynamic partitioning device for a distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0094] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] like Figure 1 As shown, an embodiment of the present invention provides a method for dynamic partitioning of a distribution network, which at least includes the following steps:

[0096] Step S1, obtaining a typical day dispatch plan of the distribution network;

[0097] Specifically, in a preferred embodiment, the obtaining of a typical day dispatch plan of the distribution network includes:

[0098] Obtain load data, distributed photovoltaic data and distribution network operation data on a typical day of the distribution network;

[0099] Among them, load data includes: load active power of the node; distributed photovoltaic data includes: predicted power of distributed photovoltaic of distributed power supply node and capacity of distributed photovoltaic of distributed power supply node; distribution network operation data includes: topological information of distribution network, impedance of branch, current upper limit of branch, voltage upper limit of node, voltage lower limit of node, active power upper limit of node and active power lower limit of node;

[0100] Constructing an optimal power flow model of the distribution network according to the distribution network operation data of the typical day;

[0101] According to the load data of the typical day and the distributed photovoltaic prediction data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

[0102] In an optional embodiment, the distribution network optimal power flow model includes an objective function and constraint conditions;

[0103] The objective function includes:

[0104]

[0105] Where L is the network loss power of the distribution network; I ij，t is the current of branch ij at the dispatching time t; r ij is the resistance of branch ij; T is the scheduling time set; ε is the branch set;

[0106] The constraints include: node power balance constraints, Ohm's law constraints, branch head end power constraints, node voltage amplitude constraints, node injection power constraints, branch current amplitude constraints and distributed photovoltaic constraints.

[0107] Preferably, the node power balance constraint is:

[0108]

[0109]

[0110] Among them, S jk，t is the active power of branch jk at the dispatching time t; S ij，t is the active power of branch ij at the dispatching time t; S j，t is the active power of node j at scheduling time t; z ij is the impedance of branch ij; I ij，t is the current of branch ij at the dispatching time t; is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the load active power of node j at scheduling time t;

[0111] The Ohm's law constraint is:

[0112] V i，t -V j，t =z ij I ij，t

[0113] Among them, V i，t is the voltage of node i at scheduling time t; V j，t is the voltage of node j at scheduling time t; z ij is the impedance of branch ij; II j，t is the current of branch ij at the dispatching time t;

[0114] The power constraint at the head end of the branch is:

[0115]

[0116] Among them, S ij，t is the active power of branch ij at the dispatching time t; V i，t is the voltage of node i at scheduling time t; is the conjugate of the current of branch ij at the dispatching time t;

[0117] The node voltage amplitude constraint is:

[0118]

[0119] in,V i is the voltage lower limit of node i; is the voltage upper limit of node i; V i，t is the voltage of node i at scheduling time t;

[0120] The node injection power constraint is:

[0121]

[0122] in, S i is the upper limit of active power of node i; The lower limit of active power of node i; S i，t is the active power of node i at scheduling time t;

[0123] The branch current amplitude constraint is:

[0124]

[0125] Among them, I ij，t is the current of branch ij at the dispatching time t; is the upper limit of the current of branch ij;

[0126] The distributed photovoltaic constraints are:

[0127]

[0128] in, is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the predicted power of the distributed photovoltaic connected to node j at the scheduling time t.

[0129] It should be noted that a typical day of a distribution network refers to a representative day selected in the distribution network operation analysis, which is used to simulate and study the operating characteristics of the distribution network in a specific time period.

[0130] In the specific implementation, in order to solve the optimal power flow model of the distribution network conveniently and effectively, the second-order cone relaxation and Big-M method are used to transform it into a mixed integer second-order cone model. Through this transformation, the model can significantly reduce the complexity of the solution and improve the efficiency of the solution while retaining the main characteristics. In addition, the commercial solver Gurobi is used to solve it, which can make full use of its efficient algorithm and powerful optimization capabilities to quickly and accurately obtain the solution of the optimal power flow model of the distribution network, which not only ensures the efficiency of the solution process, but also provides strong support for the subsequent distribution network optimization scheduling and management.

[0131] Step S2, inputting the dispatch plan into the distribution network partition model, generating the first partition scheme and the first partition score at each dispatching moment when the partition scheme score is the largest;

[0132] Specifically, in a preferred embodiment, the distribution network partition model includes an objective function;

[0133]

[0134] Among them, G is the partition scheme score; is the partition structure at the scheduling time t; I1 is the regional modularity index; ω1 is the weight of the regional modularity index; I2 is the load rate construction index; ω2 is the weight of the load rate construction index; I3 is the average load difference index; ω3 is the weight of the average load difference index; I4 is the average peak-to-valley difference rate index; ω4 is the weight of the average peak-to-valley difference rate index; I5 is the voltage regulation capability index; ω5 is the weight of the voltage regulation capability index; I6 is the power regulation capability index; ω6 is the weight of the power regulation capability index.

[0135] It can be understood that by inputting the dispatch plan into the distribution network partition model and generating the first partition scheme at each dispatch moment and the partition scheme with the maximum score, the management efficiency and dispatch optimization of the distribution network can be effectively improved. In addition, the maximized score indicates that the generated partition scheme performs well in voltage regulation, load balancing and utilization of renewable energy, providing a reliable basis for subsequent dispatch decisions.

[0136] Specifically, the regional modularity index is determined by the following formula:

[0137]

[0138] e ij =1-L ij / max((L)

[0139]

[0140]

[0141]

[0142] Among them, I1 is the regional modularity index; e ij is the electrical distance between node i and node j; m is the half value of the electrical distance between all nodes in the planning area; k i is the sum of the weights of node i; k j is the sum of the weights of node i and node j; δ(i, j) is a 0-1 variable indicating whether nodes i and j are located in the same region; L ij is the degree of association between node i and node j; max(L) is the maximum degree of association; d in is the influence degree of node n on node i; d jnis the influence degree of node n on node j; d ij is the influence degree of node j on node i; S VP，jj is the sensitivity in the sensitivity matrix associated with the voltage and active power of node j; S VQ，jj is the sensitivity in the sensitivity matrix associated with the voltage and reactive power of node j; S VP，ij is the sensitivity in the sensitivity matrix associated with the voltage and active power of node i and node j; S VQ，ij is the sensitivity in the sensitivity matrix associated with the voltage reactive power of node i and node j;

[0143] Exemplarily, the process of constructing the sensitivity matrix associated with voltage and active power includes: performing a small disturbance on the active power of each node, such as increasing or decreasing a certain amount of active power, and then recording the changes in the voltages of other nodes. This process can reflect the degree of influence of the active power change of each node on the voltage changes of other nodes, thereby constructing a sensitivity matrix associated with voltage and active power.

[0144] Exemplarily, the process of constructing the sensitivity matrix associated with voltage and reactive power includes: performing a small disturbance on the reactive power of each node, such as increasing or decreasing a certain amount of reactive power, and then recording the changes in the voltages of other nodes. This process can reflect the degree of influence of the reactive power change of each node on the voltages of other nodes, thereby constructing a sensitivity matrix associated with voltage and active power.

[0145] It is understandable that in the structure of the distribution network, the connection between nodes within each region should be relatively close, while the connection between each region should be relatively loose, so as to facilitate the management and operation of each region. Therefore, the voltage regulation capability index is a positive index. The larger the regional modularity index, the higher the closeness of the nodes in the region, the more obvious the looseness between regions, and the better the distribution network structure. This structural advantage ensures that the distribution network can quickly and effectively adjust and respond to load changes, faults or other emergencies.

[0146] Specifically, the load factor construction index is determined by the following formula:

[0147]

[0148]

[0149] Where I2 is the load factor structure index; N is the number of regions; s is the region index; f s is the load rate of each area; f av is the average load factor of each area;

[0150] Exemplarily, the load rate of each area is calculated by the ratio of the actual load in the area to the maximum load in the area; wherein the actual load in the area refers to the total active power of all load nodes in the area, and the maximum load in the area is the highest load value in the area within a specific time period.

[0151] It is understandable that the load factor construction index is an important parameter for evaluating the load status of each area of ​​the distribution network. This index is a negative index, so it is directly set to a negative value in the distribution network partition model, which means that the larger its value, the smaller the value of the load factor construction index itself, the higher the load factor of each area, and the more concentrated the distribution range of these load factors.

[0152] Specifically, the average load difference index is determined by the following formula:

[0153]

[0154] Among them, I3 is the average load difference index; N is the number of regions; s is the region index; P max is the maximum value of the maximum load in each area; P s max is the maximum load of the sth area;

[0155] It is understandable that the average load difference index, as a negative indicator, is mainly used to evaluate the load distribution between various regions in the distribution network. In the distribution network partition model, it is directly set to a negative value, which means that the larger its value is, and the smaller the value of the average load difference index itself is, the closer the load levels of various regions are, and the load distribution tends to be uniform. In addition, the smaller the value of the average load difference index itself is, the more coordinated the power supply between various regions is, which can improve the stability and reliability of the overall power system. By optimizing this indicator, the distribution network can better cope with load fluctuations and meet the power needs of users, thereby laying the foundation for more efficient energy management and sustainable development.

[0156] Specifically, the average peak-to-valley difference rate index is determined by the following formula:

[0157]

[0158] Among them, I4 is the average peak-to-valley difference rate index; N is the number of regions; s is the region index; f sp is the peak-to-valley difference rate of each region;

[0159] Optionally, the peak-to-valley difference rate of each region is calculated by subtracting the difference between the maximum load and the minimum load in the region and then dividing it by the maximum load, wherein the maximum load represents the peak load value of the region in the period, and the minimum load represents the valley load value in the period.

[0160] It is understandable that the average peak-to-valley difference rate index is a negative indicator for measuring the load fluctuation of the distribution network. In the distribution network partition model, it is directly set to a negative value, which means that the larger its value is, the smaller the value of the average load difference index itself is, indicating that the load fluctuation in the partitioned area is smaller and the load demand is more stable. The reduction in the original value of this indicator means that the impact of peak load can be better controlled and alleviated in the region, which helps to achieve more stable load management and scheduling, thereby improving the regulation ability of the distribution network when the load peak-to-valley difference is large, and further ensuring the reliability and efficiency of power supply.

[0161] Specifically, the voltage regulation capability index is determined by the following formula:

[0162]

[0163]

[0164] Among them, I5 is the voltage regulation capability index; N is the number of regions; s is the region index; is the voltage regulation capability of the region; ΔV i is the voltage regulation amount of node i in region s; is the maximum voltage regulation amount of node i in region s;

[0165] Understandably, voltage over-limit is a common key problem after a high proportion of distributed photovoltaics are connected to the distribution network. The voltage regulation capability index is defined as the ability of the adjustable active and reactive power of photovoltaic, energy storage and load resources in the region to regulate the maximum voltage deviation. The voltage regulation capability index is a positive indicator. The higher the value, the more effectively the region can balance voltage fluctuations, reflecting better voltage control performance, thus reflecting a more reasonable zoning scheme.

[0166] Specifically, the power continuous regulation capability index is determined by the following formula:

[0167]

[0168]

[0169]

[0170] Among them, I6 is the power continuous regulation capability index; N is the number of regions; s is the region index; is the power regulation capability of region s; is the adjustable power of area s; is the net load of area s at dispatch time t; is the adjustable power of distributed photovoltaic in area s at scheduling time t; is the adjustable power of the flexible load in area s at the scheduling time t.

[0171] Exemplarily, the adjustable power of the distributed photovoltaic connected to the distributed power node can be calculated by subtracting the lower limit of the distributed photovoltaic power generation from the current distributed photovoltaic power generation. The adjustable power of the flexible load can be calculated by the difference between the maximum adjustable power and the current load power, where the maximum adjustable power refers to the maximum power value that the flexible load can adjust within the scheduling time, and the current load power refers to the power actually consumed at that moment.

[0172] It is understandable that the randomness and volatility of photovoltaic and load power exacerbate the fluctuation of net load of distribution network. The power continuous regulation capability index is defined as the ability of adjustable power of multiple regulation resources in the region to cope with net load fluctuation. The power continuous regulation capability index is a positive indicator. The higher the value of this index, the more effective the regulation resources in the region can adapt to and alleviate load fluctuations, ensuring the stability and reliability of distribution network operation.

[0173] Step S3, obtaining the first voltage of each node at the first scheduling time during the operation day of the distribution network;

[0174] It should be noted that the distribution network operation day refers to the specific date of the actual operation and dispatch management of the distribution network, which is usually used to analyze and evaluate the operating status and load characteristics of the distribution network.

[0175] Step S4, judging whether the first voltage of each node exceeds the preset voltage range, if there is a node exceeding the preset voltage range, then by adjusting the weight of each evaluation index in the distribution network partition model, an updated distribution network partition model is obtained, the scheduling plan is input into the updated distribution network partition model, and a second partition scheme and a second partition score at the first scheduling moment when the partition scheme score is the largest are generated; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range;

[0176] In specific operation, when the first voltage exceeds the limit, the weight of the voltage regulation capability index of the distribution network partition model is increased, and the weights of other indicators are modified to obtain an updated distribution network partition model. The scheduling plan of the first scheduling moment is input into the updated distribution network partition model to generate the second partition scheme and the second partition score at the first scheduling moment when the partition scheme score is the largest; ensure that under the second partition scheme, the first voltage of each node does not exceed the preset voltage range.

[0177] Step S5: Calculate the difference between the first partition score and the second partition score at the first scheduling time. If the difference exceeds the set threshold, the corresponding second partition scheme is used as the actual partition scheme at the first scheduling time; if the difference does not exceed the set threshold, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling time.

[0178] It can be understood that by calculating the difference between the first partition score and the second partition score at the first scheduling moment and comparing it with the set threshold, the pros and cons of the partition scheme can be effectively evaluated. This process helps to ensure that a more reasonable and efficient partition scheme is selected in the actual operation of the distribution network, thereby improving the operating efficiency and stability of the power grid. If the difference does not exceed the preset threshold, the first partition scheme is still selected instead of the second partition scheme after weight adjustment, indicating that the necessity of partition adjustment is not strong. Even if there is a voltage limit violation, the original scheme has little effect on the overall stability and scheduling effect of the system. This selection is based on the setting of the difference threshold, which ensures that the system does not frequently adjust the partition due to minor optimization effects, thereby reducing unnecessary partition switching.

[0179] Step S6: If there is no node whose voltage exceeds the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment;

[0180] It can be understood that when no node exceeds the preset voltage range, selecting the first partitioning scheme as the actual partitioning scheme at the first scheduling moment helps maintain the operating stability and safety of the distribution network, reduces frequent adjustments to the partitioning scheme, and thus reduces the complexity of scheduling decisions.

[0181] Step S7: For other scheduling moments except the first scheduling moment, calculate the difference between the first partition score and the actual partition score at the previous scheduling moment. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

[0182] It can be understood that by calculating the difference between the first partition score at the current moment and the actual partition score at the previous scheduling moment, the stability and continuity of the partition scheme can be effectively monitored. If the difference exceeds the preset threshold, the first partition scheme at the current moment is selected as the actual partition scheme. If the difference does not exceed the threshold, the actual partition scheme at the previous moment is retained to avoid unnecessary frequent adjustments, which helps to optimize the operation and management of the distribution network and improve overall efficiency and reliability.

[0183] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0184] like Figure 2 As shown, an embodiment of the present invention provides a dynamic partitioning device for a distribution network, including: a scheduling plan acquisition module 101, a first partitioning scheme generation module 102, a second partitioning scheme generation module 103 and an actual partitioning scheme confirmation module 104.

[0185] The dispatch plan acquisition module 101 is used to acquire the dispatch plan of a typical day of the distribution network.

[0186] The first partition scheme generating module 102 is used to input the scheduling plan into the distribution network partition model, and generate the first partition scheme and the first partition score at each scheduling moment when the partition scheme score is the largest;

[0187] The second partition scheme generating module 103 is used to determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, the updated distribution network partition model is obtained by adjusting the weights of each evaluation index in the distribution network partition model, and the scheduling plan is input into the updated distribution network partition model to generate a second partition scheme and a second partition score at the first scheduling moment when the partition scheme score is the largest; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range;

[0188] The actual partition scheme confirmation module 104 is used to calculate the difference between the first partition score and the second partition score at the first scheduling moment. If the difference exceeds the set threshold, the corresponding second partition scheme is used as the actual partition scheme at the first scheduling moment; if the difference does not exceed the set threshold, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling moment; if there is no node exceeding the preset voltage range, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling moment; for other scheduling moments except the first scheduling moment, the difference between the first partition score and the actual partition score at the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

[0189] It should be noted that the embodiments of the device described above correspond to the above embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0190] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0191] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the dynamic partitioning method of the distribution network described in any one of the present invention is implemented, or, when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0192] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0193] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0194] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0195] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0196] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0197] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned methods for dynamic partitioning of a distribution network of the present invention.

[0198] Wherein, the above storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0199] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0200] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for dynamic partitioning of a distribution network, characterized in that: include: Obtain the dispatch plan for a typical day of the distribution network; Inputting the dispatch plan into the distribution network partition model to generate the first partition scheme and the first partition score at each dispatching moment when the partition scheme score is the largest; Obtain the first voltage of each node at the first scheduling time during the operation day of the distribution network; Determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, adjust the weight of each evaluation index in the distribution network partition model to obtain an updated distribution network partition model, input the scheduling plan into the updated distribution network partition model, and generate a second partition scheme and a second partition score at the first scheduling time when the partition scheme score is the largest; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range; Calculate the difference between the first partition score and the second partition score at the first scheduling time. If the difference exceeds the set threshold, the corresponding second partition scheme is used as the actual partition scheme at the first scheduling time. If the difference does not exceed the set threshold, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling time. If there is no node exceeding the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; For other scheduling moments except the first scheduling moment, the difference between the first partition score and the actual partition score at the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

2. A method for dynamic partitioning of a distribution network as claimed in claim 1, characterized in that: The obtaining of the dispatching plan of a typical day of the distribution network includes: Obtain load data, distributed photovoltaic data and distribution network operation data on a typical day of the distribution network; Among them, load data includes: load active power of the node; distributed photovoltaic data includes: predicted power of distributed photovoltaic of distributed power supply node and capacity of distributed photovoltaic of distributed power supply node; distribution network operation data includes: topological information of distribution network, impedance of branch, current upper limit of branch, voltage upper limit of node, voltage lower limit of node, active power upper limit of node and active power lower limit of node; Constructing an optimal power flow model of the distribution network according to the distribution network operation data of the typical day; According to the load data of the typical day and the distributed photovoltaic prediction data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

3. A method for dynamic partitioning of a distribution network as claimed in claim 2, characterized in that: The distribution network optimal power flow model includes an objective function and constraint conditions; The objective function includes: Where L is the network loss power of the distribution network; I ij，t is the current of branch ij at the dispatching time t; r ij is the resistance of branch ij; T is the scheduling time set; ε is the branch set; The constraints include: node power balance constraints, Ohm's law constraints, branch head end power constraints, node voltage amplitude constraints, node injection power constraints, branch current amplitude constraints and distributed photovoltaic constraints.

4. A method for dynamic partitioning of a distribution network as claimed in claim 3, characterized in that: The node power balance constraint is: Among them, S jk，t is the active power of branch jk at the dispatching time t; S ij，t is the active power of branch ij at the dispatching time t; S j，t is the active power of node j at scheduling time t; z ij is the impedance of branch ij; I ij，t is the current of branch ij at the dispatching time t; is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the load active power of node j at scheduling time t; The Ohm's law constraint is: V i，t -V j，t =z ij I ij，t Among them, V i，t is the voltage of node i at scheduling time t; V j，t is the voltage of node j at scheduling time t; z ij is the impedance of branch ij; I ij，t is the current of branch ij at the dispatching time t; The power constraint at the head end of the branch is: Among them, S ij，t is the active power of branch ij at the dispatching time t; V i，t is the voltage of node i at scheduling time t; is the conjugate of the current of branch ij at the dispatching time t; The node voltage amplitude constraint is: in, V i is the voltage lower limit of node i; is the voltage upper limit of node i; V i，t is the voltage of node i at scheduling time t; The node injection power constraint is: in, S i is the upper limit of active power of node i; The lower limit of active power of node i; S i，t is the active power of node i at scheduling time t; The branch current amplitude constraint is: Among them, I ij，t is the current of branch ij at the dispatching time t; is the upper limit of the current of branch ij; The distributed photovoltaic constraints are: in, is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the predicted power of the distributed photovoltaic connected to node j at the scheduling time t.

5. A method for dynamic partitioning of a distribution network as claimed in claim 1, characterized in that: The distribution network partition model includes an objective function; Among them, G is the partition scheme score; is the partition structure at the scheduling time t; I1 is the regional modularity index; ω1 is the weight of the regional modularity index; I2 is the load rate construction index; ω2 is the weight of the load rate construction index; I3 is the average load difference index; ω3 is the weight of the average load difference index; I4 is the average peak-to-valley difference rate index; ω4 is the weight of the average peak-to-valley difference rate index; I5 is the voltage regulation capability index; ω5 is the weight of the voltage regulation capability index; I6 is the power regulation capability index; ω6 is the weight of the power regulation capability index.

6. A method for dynamic partitioning of a distribution network as claimed in claim 5, characterized in that: The regional modularity index is determined by the following formula: e ij =1-L ij / max(L) Among them, I1 is the regional modularity index; e ij is the electrical distance between node i and node j; m is the half value of the electrical distance between all nodes in the planning area; k i is the sum of the weights of node i; k j is the sum of the weights of node i and node j; δ(i, j) is a 0-1 variable indicating whether nodes i and j are located in the same region; L ij is the degree of association between node i and node j; max(L) is the maximum degree of association; d in is the influence degree of node n on node i; dj n为 The degree of influence of node n on node j; d ij is the influence degree of node j on node i; S VP，jj is the sensitivity in the sensitivity matrix associated with the voltage and active power of node j; S VQ，jj is the sensitivity in the sensitivity matrix associated with the voltage and reactive power of node j; S VP，ij is the sensitivity in the sensitivity matrix associated with the voltage and active power of node i and node j; S VQ，ij is the sensitivity in the sensitivity matrix associated with the voltage reactive power of node i and node j; The load factor construction index is determined by the following formula: Where I2 is the load factor structure index; N is the number of regions; s is the region index; f s is the load rate of each area; f av is the average load factor of each area; The average load difference index is determined by the following formula: Among them, I3 is the average load difference index; N is the number of regions; s is the region index; P max is the maximum value of the maximum load in each area; P smax is the maximum load of the sth area; The average peak-to-valley rate indicator is determined by the following formula: Among them, I4 is the average peak-to-valley difference rate index; N is the number of regions; s is the region index; f sp is the peak-to-valley difference rate of each region; The voltage regulation capability index is determined by the following formula: Among them, I5 is the voltage regulation capability index; N is the number of regions; s is the region index; is the voltage regulation capability of region s; ΔV i is the voltage regulation amount of node i in region s; is the maximum voltage regulation amount of node i in region s; The power continuous regulation capability index is determined by the following formula: Among them, I6 is the power continuous regulation capability index; N is the number of regions; s is the region index; is the power regulation capability of region s; is the adjustable power of area s; is the net load of area s at dispatch time t; is the adjustable power of distributed photovoltaic in area s at scheduling time t; is the adjustable power of the flexible load in area s at the scheduling time t.

7. A dynamic partitioning device for a distribution network, characterized in that: include: A scheduling plan acquisition module, a first partitioning scheme generation module, a second partitioning scheme generation module and an actual partitioning scheme confirmation module; The dispatch plan acquisition module is used to obtain the dispatch plan of a typical day of the distribution network; The first partition scheme generating module is used to input the scheduling plan into the distribution network partition model to generate the first partition scheme and the first partition score at each scheduling moment when the partition scheme score is the largest; The second partition scheme generating module is used to determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, the weight of each evaluation index in the distribution network partition model is adjusted to obtain an updated distribution network partition model, and the scheduling plan is input into the updated distribution network partition model to generate a second partition scheme and a second partition score at the first scheduling time when the partition scheme score is the largest; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range; The actual partition scheme confirmation module is used to calculate the difference between the first partition score and the second partition score at the first scheduling moment. If the difference exceeds the set threshold, the corresponding second partition scheme is used as the actual partition scheme at the first scheduling moment; if the difference does not exceed the set threshold, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling moment; if there is no node exceeding the preset voltage range, the corresponding first partition scheme is used as the actual partition scheme at the first scheduling moment; for other scheduling moments except the first scheduling moment, the difference between the first partition score and the actual partition score at the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

8. A dynamic partitioning device for a power distribution network as claimed in claim 7, characterized in that: The obtaining of the dispatching plan of a typical day of the distribution network includes: Obtain load data, distributed photovoltaic data and distribution network operation data on a typical day of the distribution network; Among them, load data includes: load active power of the node; distributed photovoltaic data includes: predicted power of distributed photovoltaic of distributed power supply node and capacity of distributed photovoltaic of distributed power supply node; distribution network operation data includes: topological information of distribution network, impedance of branch, current upper limit of branch, voltage upper limit of node, voltage lower limit of node, active power upper limit of node and active power lower limit of node; Constructing an optimal power flow model of the distribution network according to the distribution network operation data of the typical day; According to the load data of the typical day and the distributed photovoltaic prediction data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the dynamic partitioning method of the distribution network described in any one of claims 1 to 6 can be implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for dynamic partitioning of a distribution network as described in any one of claims 1 to 6 can be implemented.

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